



"""simple docstring"""

import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A_						:    Tuple				=      logging.get_logger(__name__)


A_						:    List[str]				=      {
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

A_						:    List[str]				=      {
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

A_						:    str				=      '</w>'
A_						:    Dict				=      '@@ '
def  __snake_case							(	__A      :						Any     )     ->       int:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     set()
				SCREAMING_SNAKE_CASE						:							List[str]							=     word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)     )
								SCREAMING_SNAKE_CASE						:							List[str]							=     char
				return pairs


# Speech2Text2 has no max input length
A_						:    List[str]				=      {'facebook/s2t-wav2vec2-large-en-de': 1024}







class     lowerCAmelCase__						(	_UpperCAmelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		int  						= VOCAB_FILES_NAMES
	_SCREAMING_SNAKE_CASE	:		Dict  						= PRETRAINED_VOCAB_FILES_MAP
	_SCREAMING_SNAKE_CASE	:		List[Any]  						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_SCREAMING_SNAKE_CASE	:		Optional[Any]  						= ['''input_ids''', '''attention_mask''']





	def __init__(      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]="<s>"     ,       _SCREAMING_SNAKE_CASE				:       str="<pad>"     ,       _SCREAMING_SNAKE_CASE				:       str="</s>"     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]="<unk>"     ,       _SCREAMING_SNAKE_CASE				:       Tuple=False     ,       _SCREAMING_SNAKE_CASE				:       List[str]=None     ,       **_SCREAMING_SNAKE_CASE				:       List[str]     ,       )						->	Dict:





					"""simple docstring"""


					super().__init__(
					    unk_token=_SCREAMING_SNAKE_CASE     ,       bos_token=_SCREAMING_SNAKE_CASE     ,       eos_token=_SCREAMING_SNAKE_CASE     ,       pad_token=_SCREAMING_SNAKE_CASE     ,       do_lower_case=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE     ,       )

					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     do_lower_case

					with open(_SCREAMING_SNAKE_CASE     ,       encoding='utf-8'    ) as vocab_handle:
									SCREAMING_SNAKE_CASE						:							int							=     json.load(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     {v: k for k, v in self.encoder.items()}

					if merges_file is None:
									logger.info(f"""No merges files provided. {self.__class__.__name__} can only be used for decoding."""    )

									SCREAMING_SNAKE_CASE						:							Any							=     None
									SCREAMING_SNAKE_CASE						:							Dict							=     None
					else:
									with open(_SCREAMING_SNAKE_CASE     ,       encoding='utf-8'    ) as merges_handle:
													SCREAMING_SNAKE_CASE						:							Any							=     merges_handle.read().split('\n'    )[:-1]

									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     [tuple(merge.split()[:2]    ) for merge in merges]
									SCREAMING_SNAKE_CASE						:							str							=     dict(zip(_SCREAMING_SNAKE_CASE     ,       range(len(_SCREAMING_SNAKE_CASE    )    )    )    )
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     {}





	@property
	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	int:





					"""simple docstring"""


					return len(self.decoder    )





	def 			_lowerCAmelCase     (      self				:       Dict    )						->	Dict:





					"""simple docstring"""


					return dict(self.encoder     ,       **self.added_tokens_encoder    )





	def 			_lowerCAmelCase     (      self				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       str    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     tuple(token[:-1]    ) + (token[-1] + BPE_TOKEN_MERGES,)
					if token in self.cache:
									return self.cache[token]
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     get_pairs(_SCREAMING_SNAKE_CASE    )

					if not pairs:
									return token

					while True:
									SCREAMING_SNAKE_CASE						:							Any							=     min(_SCREAMING_SNAKE_CASE     ,       key=lambda _SCREAMING_SNAKE_CASE    : self.bpe_ranks.get(_SCREAMING_SNAKE_CASE     ,       float('inf'    )    )    )
									if bigram not in self.bpe_ranks:
													break
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[str]							=     bigram
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     []
									SCREAMING_SNAKE_CASE						:							Dict							=     0
									while i < len(_SCREAMING_SNAKE_CASE    ):
													try:
																	SCREAMING_SNAKE_CASE						:							List[str]							=     word.index(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
													except ValueError:
																	new_word.extend(word[i:]    )
																	break
													else:
																	new_word.extend(word[i:j]    )
																	SCREAMING_SNAKE_CASE						:							str							=     j

													if word[i] == first and i < len(_SCREAMING_SNAKE_CASE    ) - 1 and word[i + 1] == second:
																	new_word.append(first + second    )
																	i += 2
													else:
																	new_word.append(word[i]    )
																	i += 1
									SCREAMING_SNAKE_CASE						:							Dict							=     tuple(_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							List[Any]							=     new_word
									if len(_SCREAMING_SNAKE_CASE    ) == 1:
													break
									else:
													SCREAMING_SNAKE_CASE						:							List[Any]							=     get_pairs(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Any							=     ' '.join(_SCREAMING_SNAKE_CASE    )
					if word == "\n  " + BPE_TOKEN_MERGES:
									SCREAMING_SNAKE_CASE						:							int							=     '\n' + BPE_TOKEN_MERGES

					if word.endswith(_SCREAMING_SNAKE_CASE    ):
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     word.replace(_SCREAMING_SNAKE_CASE     ,       ''    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     word.replace(' '     ,       _SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							str							=     word
					return word





	def 			_lowerCAmelCase     (      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       Dict    )						->	Dict:





					"""simple docstring"""


					if self.bpe_ranks is None:
									raise ValueError(
									    'This tokenizer was instantiated without a `merges.txt` file, so'
									    ' that it can only be used for decoding, not for encoding.'
									    'Make sure to provide `merges.txt` file at instantiation to enable '
									    'encoding.'    )

					if self.do_lower_case:
									SCREAMING_SNAKE_CASE						:							List[str]							=     text.lower()

					SCREAMING_SNAKE_CASE						:							Tuple							=     text.split()

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     []
					for token in text:
									if token:
													split_tokens.extend(list(self.bpe(_SCREAMING_SNAKE_CASE    ).split(' '    )    )    )

					return split_tokens





	def 			_lowerCAmelCase     (      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       int    )						->	int:





					"""simple docstring"""


					return self.encoder.get(_SCREAMING_SNAKE_CASE     ,       self.encoder.get(self.unk_token    )    )





	def 			_lowerCAmelCase     (      self				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       List[Any]    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     self.decoder.get(_SCREAMING_SNAKE_CASE     ,       self.unk_token    )
					return result





	def 			_lowerCAmelCase     (      self				:       str     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     ' '.join(_SCREAMING_SNAKE_CASE    )

					# make sure @@ tokens are concatenated
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     ''.join(string.split(_SCREAMING_SNAKE_CASE    )    )

					return string







	def 			_lowerCAmelCase     (      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any] = None    )						->	Tuple[str]:





					"""simple docstring"""


					if not os.path.isdir(_SCREAMING_SNAKE_CASE    ):
									logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""    )
									return
					SCREAMING_SNAKE_CASE						:							int							=     os.path.join(
					    _SCREAMING_SNAKE_CASE     ,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )
					SCREAMING_SNAKE_CASE						:							str							=     os.path.join(
					    _SCREAMING_SNAKE_CASE     ,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']    )

					with open(_SCREAMING_SNAKE_CASE     ,       'w'     ,       encoding='utf-8'    ) as f:
									f.write(json.dumps(self.encoder     ,       indent=2     ,       sort_keys=_SCREAMING_SNAKE_CASE     ,       ensure_ascii=_SCREAMING_SNAKE_CASE    ) + '\n'    )

					SCREAMING_SNAKE_CASE						:							List[str]							=     0
					if self.bpe_ranks is None:
									return (vocab_file,)

					with open(_SCREAMING_SNAKE_CASE     ,       'w'     ,       encoding='utf-8'    ) as writer:
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,       key=lambda _SCREAMING_SNAKE_CASE    : kv[1]    ):
													if index != token_index:
																	logger.warning(
																	    f"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
																	    ' Please check that the tokenizer is not corrupted!'    )
																	SCREAMING_SNAKE_CASE						:							Any							=     token_index
													writer.write(' '.join(_SCREAMING_SNAKE_CASE    ) + '\n'    )
													index += 1

					return (vocab_file, merges_file)












"""simple docstring"""





import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE__   =  logging.getLogger(__name__)

@dataclass
class 						lowercase   :
      _SCREAMING_SNAKE_CASE							      = 42
      _SCREAMING_SNAKE_CASE							      = 42
      _SCREAMING_SNAKE_CASE							      = 42







@dataclass
class 						lowercase   :
      _SCREAMING_SNAKE_CASE							      = 42
      _SCREAMING_SNAKE_CASE							      = 42
      _SCREAMING_SNAKE_CASE							      = None
      _SCREAMING_SNAKE_CASE							      = None







class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = 'train'
      _SCREAMING_SNAKE_CASE							      = 'dev'
      _SCREAMING_SNAKE_CASE							      = 'test'







class 						lowercase   :





      @staticmethod
      def        _snake_case     (			lowercase ,		lowercase					)						->	List[InputExample]:
            raise NotImplementedError





      @staticmethod
      def        _snake_case     (			lowercase					)						->	List[str]:
            raise NotImplementedError





      @staticmethod
      def        _snake_case     (			lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase=False ,		lowercase="[CLS]" ,		lowercase=1 ,		lowercase="[SEP]" ,		lowercase=False ,		lowercase=False ,		lowercase=0 ,		lowercase=0 ,		lowercase=-100 ,		lowercase=0 ,		lowercase=True ,		)						->	List[InputFeatures]:
            lowerCAmelCase    		=			{label: i for i, label in enumerate(lowercase					)}

            lowerCAmelCase    		=			[]
            for ex_index, example in enumerate(lowercase					):
                  if ex_index % 10_000 == 0:
                        logger.info("""Writing example %d of %d""" ,		lowercase ,		len(lowercase					)					)

                  lowerCAmelCase    		=			[]
                  lowerCAmelCase    		=			[]
                  for word, label in zip(example.words ,		example.labels					):
                        lowerCAmelCase    		=			tokenizer.tokenize(lowercase					)

                        # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                        if len(lowercase					) > 0:
                              tokens.extend(lowercase					)
                              # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                              label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(lowercase					) - 1)					)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
                  lowerCAmelCase    		=			tokenizer.num_special_tokens_to_add()
                  if len(lowercase					) > max_seq_length - special_tokens_count:
                        lowerCAmelCase    		=			tokens[: (max_seq_length - special_tokens_count)]
                        lowerCAmelCase    		=			label_ids[: (max_seq_length - special_tokens_count)]

                  # The convention in BERT is:
                  # (a) For sequence pairs:
                  #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
                  #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
                  # (b) For single sequences:
                  #  tokens:   [CLS] the dog is hairy . [SEP]
                  #  type_ids:   0   0   0   0  0     0   0
                  #
                  # Where "type_ids" are used to indicate whether this is the first
                  # sequence or the second sequence. The embedding vectors for `type=0` and
                  # `type=1` were learned during pre-training and are added to the wordpiece
                  # embedding vector (and position vector). This is not *strictly* necessary
                  # since the [SEP] token unambiguously separates the sequences, but it makes
                  # it easier for the model to learn the concept of sequences.
                  #
                  # For classification tasks, the first vector (corresponding to [CLS]) is
                  # used as the "sentence vector". Note that this only makes sense because
                  # the entire model is fine-tuned.
                  tokens += [sep_token]
                  label_ids += [pad_token_label_id]
                  if sep_token_extra:
                        # roberta uses an extra separator b/w pairs of sentences
                        tokens += [sep_token]
                        label_ids += [pad_token_label_id]
                  lowerCAmelCase    		=			[sequence_a_segment_id] * len(lowercase					)

                  if cls_token_at_end:
                        tokens += [cls_token]
                        label_ids += [pad_token_label_id]
                        segment_ids += [cls_token_segment_id]
                  else:
                        lowerCAmelCase    		=			[cls_token] + tokens
                        lowerCAmelCase    		=			[pad_token_label_id] + label_ids
                        lowerCAmelCase    		=			[cls_token_segment_id] + segment_ids

                  lowerCAmelCase    		=			tokenizer.convert_tokens_to_ids(lowercase					)

                  # The mask has 1 for real tokens and 0 for padding tokens. Only real
                  # tokens are attended to.
                  lowerCAmelCase    		=			[1 if mask_padding_with_zero else 0] * len(lowercase					)

                  # Zero-pad up to the sequence length.
                  lowerCAmelCase    		=			max_seq_length - len(lowercase					)
                  if pad_on_left:
                        lowerCAmelCase    		=			([pad_token] * padding_length) + input_ids
                        lowerCAmelCase    		=			([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                        lowerCAmelCase    		=			([pad_token_segment_id] * padding_length) + segment_ids
                        lowerCAmelCase    		=			([pad_token_label_id] * padding_length) + label_ids
                  else:
                        input_ids += [pad_token] * padding_length
                        input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                        segment_ids += [pad_token_segment_id] * padding_length
                        label_ids += [pad_token_label_id] * padding_length

                  assert len(lowercase					) == max_seq_length
                  assert len(lowercase					) == max_seq_length
                  assert len(lowercase					) == max_seq_length
                  assert len(lowercase					) == max_seq_length

                  if ex_index < 5:
                        logger.info("""*** Example ***"""					)
                        logger.info("""guid: %s""" ,		example.guid					)
                        logger.info("""tokens: %s""" ,		""" """.join([str(lowercase					) for x in tokens]					)					)
                        logger.info("""input_ids: %s""" ,		""" """.join([str(lowercase					) for x in input_ids]					)					)
                        logger.info("""input_mask: %s""" ,		""" """.join([str(lowercase					) for x in input_mask]					)					)
                        logger.info("""segment_ids: %s""" ,		""" """.join([str(lowercase					) for x in segment_ids]					)					)
                        logger.info("""label_ids: %s""" ,		""" """.join([str(lowercase					) for x in label_ids]					)					)

                  if "token_type_ids" not in tokenizer.model_input_names:
                        lowerCAmelCase    		=			None

                  features.append(
                      InputFeatures(
                          input_ids=lowercase ,		attention_mask=lowercase ,		token_type_ids=lowercase ,		label_ids=lowercase					)					)
            return features


if is_torch_available():
       import torch
       from torch import nn
       from torch.utils.data import Dataset

       class 						lowercase   (   _UpperCAmelCase   ):
             _SCREAMING_SNAKE_CASE							      = 42
             _SCREAMING_SNAKE_CASE							      = nn.CrossEntropyLoss().ignore_index





             def __init__(			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase = None ,		lowercase=False ,		lowercase = Split.train ,		)						->	List[str]:
                   # Load data features from cache or dataset file
                   lowerCAmelCase    		=			os.path.join(
                       lowercase ,		"""cached_{}_{}_{}""".format(mode.value ,		tokenizer.__class__.__name__ ,		str(lowercase					)					) ,		)

                   # Make sure only the first process in distributed training processes the dataset,
                   # and the others will use the cache.
                   lowerCAmelCase    		=			cached_features_file + """.lock"""
                   with FileLock(lowercase					):
                         if os.path.exists(lowercase					) and not overwrite_cache:
                               logger.info(f'Loading features from cached file {cached_features_file}'					)
                               lowerCAmelCase    		=			torch.load(lowercase					)
                         else:
                               logger.info(f'Creating features from dataset file at {data_dir}'					)
                               lowerCAmelCase    		=			token_classification_task.read_examples_from_file(lowercase ,		lowercase					)
                               # TODO clean up all this to leverage built-in features of tokenizers
                               lowerCAmelCase    		=			token_classification_task.convert_examples_to_features(
                                   lowercase ,		lowercase ,		lowercase ,		lowercase ,		cls_token_at_end=bool(model_type in ["""xlnet"""]					) ,		cls_token=tokenizer.cls_token ,		cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0 ,		sep_token=tokenizer.sep_token ,		sep_token_extra=lowercase ,		pad_on_left=bool(tokenizer.padding_side == """left"""					) ,		pad_token=tokenizer.pad_token_id ,		pad_token_segment_id=tokenizer.pad_token_type_id ,		pad_token_label_id=self.pad_token_label_id ,		)
                               logger.info(f'Saving features into cached file {cached_features_file}'					)
                               torch.save(self.features ,		lowercase					)





             def __len__(			self					)						->	int:
                   return len(self.features					)





             def __getitem__(			self ,		lowercase					)						->	InputFeatures:
                   return self.features[i]


if is_tf_available():
       import tensorflow as tf

       class 						lowercase   :
             _SCREAMING_SNAKE_CASE							      = 42
             _SCREAMING_SNAKE_CASE							      = -100





             def __init__(			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase = None ,		lowercase=False ,		lowercase = Split.train ,		)						->	Any:
                   lowerCAmelCase    		=			token_classification_task.read_examples_from_file(lowercase ,		lowercase					)
                   # TODO clean up all this to leverage built-in features of tokenizers
                   lowerCAmelCase    		=			token_classification_task.convert_examples_to_features(
                       lowercase ,		lowercase ,		lowercase ,		lowercase ,		cls_token_at_end=bool(model_type in ["""xlnet"""]					) ,		cls_token=tokenizer.cls_token ,		cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0 ,		sep_token=tokenizer.sep_token ,		sep_token_extra=lowercase ,		pad_on_left=bool(tokenizer.padding_side == """left"""					) ,		pad_token=tokenizer.pad_token_id ,		pad_token_segment_id=tokenizer.pad_token_type_id ,		pad_token_label_id=self.pad_token_label_id ,		)

                   def gen():
                         for ex in self.features:
                               if ex.token_type_ids is None:
                                     yield (
                                         {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                                         ex.label_ids,
                                     )
                               else:
                                     yield (
                                         {
                                             "input_ids": ex.input_ids,
                                             "attention_mask": ex.attention_mask,
                                             "token_type_ids": ex.token_type_ids,
                                         },
                                         ex.label_ids,
                                     )

                   if "token_type_ids" not in tokenizer.model_input_names:
                         lowerCAmelCase    		=			tf.data.Dataset.from_generator(
                             lowercase ,		({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa) ,		(
                                 {"""input_ids""": tf.TensorShape([None]					), """attention_mask""": tf.TensorShape([None]					)},
                                 tf.TensorShape([None]					),
                             ) ,		)
                   else:
                         lowerCAmelCase    		=			tf.data.Dataset.from_generator(
                             lowercase ,		({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa) ,		(
                                 {
                                     """input_ids""": tf.TensorShape([None]					),
                                     """attention_mask""": tf.TensorShape([None]					),
                                     """token_type_ids""": tf.TensorShape([None]					),
                                 },
                                 tf.TensorShape([None]					),
                             ) ,		)





             def        _snake_case     (			self					)						->	List[str]:
                   lowerCAmelCase    		=			self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features					)					)					)

                   return self.dataset





             def __len__(			self					)						->	Optional[int]:
                   return len(self.features					)





             def __getitem__(			self ,		lowercase					)						->	InputFeatures:
                   return self.features[i]






